Can apparent diffusion coefficient (ADC) distinguish breast cancer from benign breast findings? A meta-analysis based on 13 847 lesions

The purpose of the present meta-analysis was to provide evident data about use of Apparent Diffusion Coefficient (ADC) values for distinguishing malignant and benign breast lesions.

R E S E A R C H A R T I C L E Open Access

Can apparent diffusion coefficient (ADC)

distinguish breast cancer from benign

breast findings? A meta-analysis based on

13 847 lesions

Alexey Surov1,2*† , Hans Jonas Meyer1†and Andreas Wienke3†

Abstract

Background: The purpose of the present meta-analysis was to provide evident data about use of Apparent

Diffusion Coefficient (ADC) values for distinguishing malignant and benign breast lesions.

Methods: MEDLINE library and SCOPUS database were screened for associations between ADC and malignancy/ benignancy of breast lesions up to December 2018 Overall, 123 items were identified The following data were extracted from the literature: authors, year of publication, study design, number of patients/lesions, lesion type, mean value and standard deviation of ADC, measure method, b values, and Tesla strength.

The methodological quality of the 123 studies was checked according to the QUADAS-2 instrument The meta-analysis was undertaken by using RevMan 5.3 software DerSimonian and Laird random-effects models with inverse-variance weights were used without any further correction to account for the heterogeneity between the studies Mean ADC values including 95% confidence intervals were calculated separately for benign and malign lesions Results: The acquired 123 studies comprised 13,847 breast lesions Malignant lesions were diagnosed in 10,622 cases (76.7%) and benign lesions in 3225 cases (23.3%) The mean ADC value of the malignant lesions was 1.03 ×

10− 3mm2/s and the mean value of the benign lesions was 1.5 × 10− 3mm2/s The calculated ADC values of benign lesions were over the value of 1.00 × 10− 3mm2/s This result was independent on Tesla strength, choice of b values, and measure methods (whole lesion measure vs estimation of ADC in a single area).

Conclusion: An ADC threshold of 1.00 × 10− 3mm2/s can be recommended for distinguishing breast cancers from benign lesions.

Keywords: Breast cancer, ADC, MRI

Background

Magnetic resonance imaging (MRI) plays an essential

diagnostic role in breast cancer (BC) [ 1 , 2 ] MRI has

been established as the most sensitive diagnostic

modal-ity in breast imaging [ 1 – 3 ] Furthermore, MRI can also

predict response to treatment in BC [ 4 ] However, it has

a high sensitivity but low specificity [ 5 ] Therefore, MRI can often not distinguish malignant and benign breast lesions Numerous studies reported that diffusion-weighted imaging (DWI) has a great diagnostic potential and can better characterize breast lesions than conven-tional MRI [ 6 – 8 ] DWI is a magnetic resonance imaging (MRI) technique based on measure of water diffusion in tissues [ 9 ] Furthermore, restriction of water diffusion can be quantified by apparent diffusion coefficient (ADC) [ 9 , 10 ] It has been shown that malignant tumors have lower values in comparison to benign lesions [ 7 ].

In addition, according to the literature, ADC is associ-ated with several histopathological features, such as cell

© The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made The Creative Commons Public Domain Dedication waiver

* Correspondence:Alexey.Surov@medizin.uni-leipzig.de

†Alexey Surov, Hans Jonas Meyer and Andreas Wienke contributed equally to

this work

1

Department of Diagnostic and Interventional Radiology, University of

Leipzig, Liebigstr 20, 04103 Leipzig, Germany

2Department of Diagnostic and Interventional Radiology, Ulm University

Medical Center, Albert-Einstein-Allee 23, 89081 Ulm, Germany

Full list of author information is available at the end of the article

count and expression of proliferation markers, in

differ-ent tumors [ 11 , 12 ].

However, use of ADC for discrimination BC and

benign breast lesions is difficult because of several

problems Firstly, most reports regarding ADC in

sev-eral breast cancers and benign breast lesions

investi-gated relatively small patients/lesions samples.

Secondly, the studies had different proportions of

ma-lignant and benign lesions Thirdly and most

import-antly, the reported ADC threshold values and as well

specificity, sensitivity, and accuracy values ranged

sig-nificantly between studies For example, in the study

of Aribal et al., 129 patients with 138 lesions (benign n =

63; malignant n = 75) were enrolled [ 13 ] The authors

re-ported the optimal ADC cut-off as 1.118 × 10− 3mm2/s

with sensitivity and specificity 90.67, and 84.13%

respect-ively [ 13 ] In a study by Arponen et al., which investigated

112 patients (23 benign and 114 malignant lesions), the

ADC threshold was 0.87 × 10− 3mm2/s with 95.7%

sensi-tivity, 89.5% specificity and overall accuracy of 89.8% [ 14 ].

Cakir et al reported in their study with 52 women and 55 breast lesions (30 malignant, 25 benign) an optimal ADC threshold as ≤1.23 × 10− 3mm2/s (sensitivity = 92.85%, spe-cificity = 54.54%, positive predictive value = 72.22%, nega-tive predicnega-tive value = 85.71%, and accuracy = 0.82) [ 15 ] Finally, different MRI scanners, Tesla strengths and b values were used in the reported studies, which are known

to have a strong influence in ADC measurements These facts question the possibility to use the reported ADC thresholds in clinical practice.

To overcome these mentioned shortcomings, the pur-pose of the present meta-analysis was to provide evident data about use of ADC values for distinguishing malig-nant and benign breast lesions.

Methods

Data acquisition and proving Figure 1 shows the strategy of data acquisition MED-LINE library and SCOPUS database were screened for associations between ADC and malignancy/benignancy

Fig 1 PRISMA flow chart of the data acquisition

of breast lesions up to December 2018 The following

search terms/combinations were as follows:

“DWI or diffusion weighted imaging or

diffusion-weighted imaging or ADC or apparent diffusion coefficient

AND breast cancer OR breast carcinoma OR mammary

cancer OR breast neoplasm OR breast tumor” Secondary

references were also manually checked and recruited The

Preferred Reporting Items for Systematic Reviews and

Meta-Analyses statement (PRISMA) was used for the

re-search [ 16 ].

Overall, the primary search identified 1174 records.

The abstracts of the items were checked Inclusion

criteria for this work were as follows:

– Data regarding ADC derived from diffusion

weighted imaging (DWI);

– Available mean and standard deviation values of

ADC;

– Original studies investigated humans;

– English language.

Overall, 127 items met the inclusion criteria Other

1017 records were excluded from the analysis Exclusion criteria were as follows:

– studies unrelated to the research subjects;

– studies with incomplete data;

– non-English language;

– duplicate publications;

– experimental animals and in vitro studies;

– review, meta-analysis and case report articles;

The following data were extracted from the literature: authors, year of publication, study design, number of pa-tients/lesions, lesion type, mean value and standard devi-ation of ADC, and Tesla strength.

Meta-analysis

On the first step, the methodological quality of the 123 studies was checked according to the Quality Assess-ment of Diagnostic Studies (QUADAS-2) instruAssess-ment

Fig 3 Funnel plot of the publication bias

Fig 2 QUADAS-2 quality assessment of the included studies

Table 1 Studies inclujded into the meta-analysis

Author, years [Ref.] Malignant

lesions, n

benign lesions, n

Study design

Tesla strength

Arponen et al., 2015 [14] 114 23 retrospective 3

Baltzer et al., 2010 [25] 54 27 retrospective 1.5

Bokacheva et al.,

2014 [30]

Caivano et al., 2015 [33] 67 43 retrospective 3

1.5

Costantini et al.,

2012 [44]

Costantini et al.,

2010 [45]

de Almeida et al.,

2017 [46]

Eghtedari et al.,

2016 [48]

1.5

Fanariotis et al.,

2018 [54]

Fornasa et al., 2011 [55] 35 43 retrospective 1.5

Guatelli et al., 2017 [57] 161 91 retrospective 1.5

Table 1 Studies inclujded into the meta-analysis (Continued)

Author, years [Ref.] Malignant

lesions, n

benign lesions, n

Study design

Tesla strength

Horvat et al., 2018 [60] 218 130 retrospective 3

Imamura et al., 2010 [64] 16 11 retrospective 1.5

1.5 Jiang et al., 2018 [68] 171 104 retrospective 1.5

1.5 Kawashima et al.,

2017 [72]

Ei Khouli et al., 2010 [73] 101 33 retrospective 3

Köremezli Keskin et al.,

2018 [80]

Matsubayashi et al.,

2010 [89]

Montemezzi et al.,

2018 [91]

[ 17 ] independently by two observers (A.S and H.J.M.) The results of QUADAS-2 assessment are shown in Fig 2 The quality of most studies showed an overall low risk of bias.

On the second step, the reported ADC values (mean and standard deviation) were acquired from the papers Thirdly, the meta-analysis was undertaken by using RevMan 5.3 [RevMan 2014 The Cochrane Collaboration Review Manager Version 5.3.] Heterogeneity was calcu-lated by means of the inconsistency index I2[ 18 , 19 ] In

a subgroup analysis, studies were stratified by tumor type In addition, DerSimonian and Laird random-effects models with inverse-variance weights were used without any further correction [ 20 ] to account for the heterogen-eity between the studies (Fig 3 ) Mean ADC values including 95% confidence intervals were calculated sep-arately for benign and malign lesions.

Results

Of the included 123 studies, 101 (82.1%) were retro-spective and 22 (17.9%) proretro-spective (Table 1 ) The stud-ies represented almost all continents and originated from Asia (n = 77, 62.6%), Europe (n = 23, 18.7%), North America (n = 19, 15.5%), South America (n = 3, 2.4%), and Africa (n = 1, 0.8%) Different 1.5 T scanners were used in 53 (43.1%) studies, 3 T scanners in 63 reports (51.2%), and in 7 studies (5.7%) both 1.5 and 3 T scanners were used Overall, 68 studies (55.3%) were performed/re-ported in the years 2015–2018, 46 studies (37.4%) in the years 2010–2014, and 9 studies (7.3%) in the years 2000– 2009.

The acquired 123 studies comprised 13,847 breast le-sions Malignant lesions were diagnosed in 10,622 cases (76.7%) and benign lesions in 3225 cases (23.3%) The mean ADC value of the malignant lesions was 1.03 ×

10− 3mm2/s and the mean value of the benign lesions was 1.5 × 10− 3mm2/s (Figs 4 and 5 ) Figure 6 shows the distribution of ADC values in malignant and benign lesions The ADC values of the two groups overlapped

Table 1 Studies inclujded into the meta-analysis (Continued)

Author, years [Ref.] Malignant

lesions, n

benign lesions, n

Study design

Tesla strength 1.5

Partridge et al.,

2018 [105]

1.5 Partridge et al., 2011

[106]

Partridge et al., 2010

[107]

Partridge et al.,

2010 [108]

Ramírez-Galván et al.,

2015 [113]

Roknsharifi et al.,

2018 [115]

Rubesova et al.,

2006 [116]

Satake et al., 2011 [118] 88 27 retrospective 3

Sonmez et al., 2011 [123] 25 20 retrospective 1.5

Woodhams et al.,

2009 [133]

Table 1 Studies inclujded into the meta-analysis (Continued)

Author, years [Ref.] Malignant

lesions, n

benign lesions, n

Study design

Tesla strength Yabuuchi et al.,

2006 [135]

1.5 Zhang et al., 2019 [138] 136 74 retrospective 3

Fig 4 Forrest plots of ADC values reported for benign breast lesions

significantly However, there were no benign lesions under the ADC value of 1.00 × 10− 3mm2/s.

On the next step ADC values between malignant and benign breast lesions were compared in dependence on Tesla strength Overall, 5854 lesions were investigated

by 1.5 T scanners and 7061 lesions by 3 T scanners In

932 lesions, the exact information regarding Tesla strength was not given In the subgroup investigated by 1.5 T scanners, the mean ADC value of the malignant lesions (n = 4093) was 1.05 × 10− 3mm2/s and the mean value of the benign lesions (n = 1761) was 1.54 × 10− 3

mm2/s (Fig 7 ) The ADC values of the benign lesions were upper the ADC value of 1.00 × 10− 3mm2/s.

In the subgroup investigated by 3 T scanners, the mean ADC values of the malignant lesions (n = 5698) was 1.01 × 10− 3mm2/s and the mean value of the benign lesions (n = 1363) was 1.46 × 10− 3mm2/s (Fig 8 ) Again

in this subgroup, there were no benign lesions under the ADC value of 1.00 × 10− 3mm2/s.

Furthermore, cumulative ADC mean values were cal-culated in dependence on choice of upper b values Overall, there were three large subgroups: b600 (426 malignant and 629 benign lesions), b750–850 (4015 malignant and 1230 benign lesions), and b1000 (4396 malignant and 1059 benign lesions) As shown in Fig 9 , the calculated ADC values of benign lesions were over the value 1.00 × 10− 3mm2/s in every subgroup.

Finally, ADC values of malignant and benign lesions obtained by single measure in an isolated selected area

or ROI (region of interest) and whole lesion measure were analyzed Single ROI measure was performed for 10,882 lesions (8037 malignant and 2845 benign lesions) and whole lesion analysis was used in 2442 cases (1996 malignant and 446 benign lesions) Also in this sub-group, the ADC values of the benign lesions were above the ADC value of 1.00 × 10− 3mm2/s (Fig 10 ).

Discussion

The present analysis investigated ADC values in be-nign and malignant breast lesions in the largest co-hort to date It addresses a key question as to whether or not imaging parameters, in particular ADC can reflect histopathology of breast lesions If

so, then ADC can be used as a validated imaging bio-marker in breast diagnostics The possibility to stratify breast lesions on imaging is very important and can

in particular avoid unnecessary biopsies As shown in our analysis, previously, numerous studies investigated this question Interestingly, most studies were re-ported in the years 2015–2018, which underlines the importance and actuality of the investigated clinical problem However, as mentioned above, their results were inconsistent There was no given threshold of an ADC value, which could be used in a clinical setting. Fig 5 Forrest plots of ADC values reported for malignant

breast lesions

Most reports indicated that malignant lesions have

lower ADC values than benign findings but there was

a broad spectrum of ADC threshold values to

dis-criminate benign and malignant breast lesions

Fur-thermore, the published results were based on

analyses of small numbers of lesions and, therefore,

cannot be apply as evident This limited the

possibil-ity to use ADC as an effective diagnostic tool in

breast imaging.

Many causes can be responsible for the

controver-sial data There are no general recommendations

re-garding use of DWI in breast MRI i.e Tesla

strengths, choice of b values etc It is known that all

the technical parameters can influence DWI and ADC

values [ 142 ] Therefore, the reported data cannot

apply for every situation For example, ADC threshold

values obtained on 1.5 T scanners cannot be

trans-ferred one-to-one to lesions on 3 T.

Furthermore, previous reports had different

propor-tions of benign and malignant lesions comprising

various entities It is well known that some benign breast lesions like abscesses have very low ADC values [ 143 ] and some breast cancers, such as mucin-ous carcinomas, show high ADC values [ 97 , 144 ] Furthermore, it has been also shown that invasive ductal and lobular carcinomas had statistically signifi-cant lower ADC values in comparison to ductal car-cinoma in situ [ 145 ] In addition, also carcinomas with different hormone receptor statuses demonstrate different ADC values [ 115 , 119 ] Therefore, the exact proportion of analyzed breast lesions is very import-ant This suggests also that analyses of ADC values between malignant and benign breast lesions should include all possible lesions All the facts can explain controversial results of the previous studies but can-not help in a real clinical situation on a patient level basis.

Recently, a meta-analysis about several DWI tech-niques like diffusion-weighted imaging, diffusion tensor imaging (DTI), and intravoxel incoherent motion (IVIM) Fig 6 Comparison of ADC values between malignant and benign breast lesions in the overall sample

Fig 7 Comparison of ADC values between malignant and benign breast lesions investigated by 1.5 T scanners

in breast imaging was published [ 146 ] It was reported

that these techniques were able to discriminate between

malignant and benign lesions with a high sensitivity and

specificity [ 146 ] However, the authors included only

studies with provided sensitivity/specificity data

Fur-thermore, no threshold values were calculated for

dis-criminating malignant and benign breast lesions.

Therefore, no recommendations regarding practical use

of DWI in clinical setting could be given.

The present analysis included all published data

about DWI findings/ADC values of different breast

le-sions and, therefore, in contrast to the previous

re-ports, did not have selection bias It showed that the

mean values of benign breast lesions were no lower

than 1.00 × 10− 3mm2/s Therefore, this value can be

used for distinguishing BC from benign findings

Fur-thermore, this result is independent from Tesla

strength, measure methods and from the choice of b values This fact is very important and suggests that this cut-off can be used in every clinical situation.

We could not find a further threshold in the upper area of ADC values because malignant and benign le-sions overlapped significantly However, most malignant lesions have ADC values under 2.0 × 10− 3mm2/s As shown, no real thresholds can be found in the area be-tween 1.00 and 2.00 × 10− 3mm2/s for discrimination malignant and benign breast lesions.

There are some inherent limitations of the present study to address Firstly, the meta- analysis is based upon published results in the literature There might

be a certain publication bias because there is a trend

to report positive or significant results; whereas stud-ies with insignificant or negative results are often rejected or are not submitted Secondly, there is the Fig 8 Comparison of ADC values between malignant and benign breast lesions investigated by 3 T scanners

Fig 9 Comparison of ADC values between malignant and benign breast lesions in dependence on the choice of b values

restriction to published papers in English language.

Approximately 50 studies could therefore not be

cluded in the present analysis Thirdly, the study

in-vestigated the widely used DWI technique using 2

b-values However, more advanced MRI sequences, such

as intravoxel-incoherent motion and diffusion-kurtosis

imaging have been developed, which might show a

better accuracy in discriminating benign from

malig-nant tumors Yet, there are few studies using these

sequences and thus no comprehensive analysis can be

made.

Conclusion

An ADC threshold of 1.0 × 10− 3mm2/s can be

recom-mended for distinguishing breast cancers from benign

lesions This result is independent on Tesla strength,

choice of b values, and measure methods.

Abbreviations

ADC:Apparent diffusion coefficient; BC: Breast cancer; MRI: Magnetic

resonance imaging

Acknowledgements

None

Authors’ contributions

AS, HJM, AW made substantial contributions to conception and design, or

acquisition of data, or analysis and interpretation of data; HJM, AW been

involved in drafting the manuscript or revising it critically for important

intellectual content; HJM, AW given final approval of the version to be

published Each author should have participated sufficiently in the work to

take public responsibility for appropriate portions of the content; and AS,

HJM, AW agreed to be accountable for all aspects of the work in ensuring

that questions related to the accuracy or integrity of any part of the work

are appropriately investigated and resolved All authors read and approved

the final manuscript

Funding

None

Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request

Ethics approval and consent to participate Not applicable

Consent for publication Not Applicable Competing interests The authors declare that they have no competing interests

Author details

1Department of Diagnostic and Interventional Radiology, University of Leipzig, Liebigstr 20, 04103 Leipzig, Germany.2Department of Diagnostic and Interventional Radiology, Ulm University Medical Center,

Albert-Einstein-Allee 23, 89081 Ulm, Germany.3Institute of Medical Epidemiology, Biostatistics, and Informatics, Martin-Luther-University Halle-Wittenberg, Magdeburger Str 8, 06097 Halle, Germany

Received: 7 May 2019 Accepted: 24 September 2019

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